# load COVID state-level data from NYTcv_states <-as.data.frame(read.csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv"))# load state population datastate_pops <-as.data.frame(read.csv("https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv"))state_pops$abb <- state_pops$statestate_pops$state <- state_pops$state_namestate_pops$state_name <-NULL### FINISH THE CODE HEREcv_states <-merge(cv_states, state_pops, by ="state")
Look at data
dim(cv_states)
[1] 58094 9
head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL
2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL
3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL
4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL
5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL
6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL
# format the datecv_states$date <-as.Date(cv_states$date, format="%Y-%m-%d")# format the state and state abbreviation (abb) variablesstate_list <-unique(cv_states$state)cv_states$state <-factor(cv_states$state, levels = state_list)abb_list <-unique(cv_states$abb)cv_states$abb <-factor(cv_states$abb, levels = abb_list)### FINISH THE CODE HERE # order the data first by state, second by datecv_states = cv_states[order(cv_states$state, cv_states$date),]# Confirm the variables are now correctly formattedstr(cv_states)
# Inspect the range values for each variable. What is the date range? The range of cases and deaths?head(cv_states)
state date fips cases deaths geo_id population pop_density abb
1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL
597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL
282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL
12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL
266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL
78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL
summary(cv_states)
state date fips cases
Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1
Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125
California : 1154 Median :2021-09-11 Median :29.00 Median : 418120
Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941
Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318
Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158
(Other) :51184
deaths geo_id population pop_density
Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292
1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659
Median : 5901 Median :29.00 Median : 4468402 Median : 107.860
Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031
3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511
Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120
NA's :1106
abb
WA : 1158
IL : 1155
CA : 1154
AZ : 1153
MA : 1147
WI : 1143
(Other):51184
min(cv_states$date)
[1] "2020-01-21"
max(cv_states$date)
[1] "2023-03-23"
The date range is from 2020-01-21 to 2023-03-23. The range of cases is from 1 to 12169158, and the range of deaths is from 0 ro 104277.
Add and correct outliers
# Add variables for new_cases and new_deaths:for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),]# add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1]### FINISH THE CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j -1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j -1] }# include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths}# Focus on recent datescv_states <- cv_states %>% dplyr::filter(date >="2021-06-01")
### FINISH THE CODE HERE# Inspect outliers in new_cases using plotlyp1<-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) +geom_boxplot() +geom_point(size = .5, alpha =0.5)ggplotly(p1)
p1<-NULL# to clear from workspacep2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_boxplot() +geom_point(size = .5, alpha =0.5)ggplotly(p2)
p2<-NULL# to clear from workspace
# set negative new case or death counts to 0cv_states$new_cases[cv_states$new_cases<0] =0cv_states$new_deaths[cv_states$new_deaths<0] =0# Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths`for (i in1:length(state_list)) { cv_subset =subset(cv_states, state == state_list[i])# add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1]### FINISH CODE HEREfor (j in2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$new_cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$new_deaths[j-1] }# include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths}# Smooth new countscv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %>%round(digits =0)cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %>%round(digits =0)
# Inspect data again interactivelyp2<-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) +geom_boxplot() +geom_point(size = .5, alpha =0.5)ggplotly(p2)
### FINISH CODE HERE# add population normalized (by 100,000) counts for each variablecv_states$per100k =as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1))cv_states$newper100k =as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1))
# add a naive_CFR variable = deaths / casescv_states = cv_states %>%mutate(naive_CFR =round((deaths*100/cases),2))# create a `cv_states_today` variablecv_states_today =subset(cv_states, date==max(cv_states$date))
Scatterplots
Explore (plot_ly)
### FINISH CODE HERE# pop_density vs. casescv_states_today %>%plot_ly(x =~pop_density, y =~cases, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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# filter out "District of Columbia"cv_states_today_filter <- cv_states_today %>%filter(state!="District of Columbia")# pop_density vs. cases after filteringcv_states_today_filter %>%plot_ly(x =~pop_density, y =~cases, type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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# pop_density vs. deathsper100kcv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5))
Warning: Ignoring 1 observations
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# Adding hoverinfocv_states_today_filter %>%plot_ly(x =~pop_density, y =~deathsper100k,type ='scatter', mode ='markers', color =~state,size =~population, sizes =c(5, 70), marker =list(sizemode='diameter', opacity=0.5),hoverinfo ='text',text =~paste( paste(state, ":", sep=""), paste(" Cases per 100k: ", per100k, sep="") , paste(" Deaths per 100k: ", deathsper100k, sep=""), sep ="<br>")) %>%layout(title ="Population-normalized COVID-19 deaths (per 100k) vs. population density for US states",yaxis =list(title ="Deaths per 100k"), xaxis =list(title ="Population Density"),hovermode ="compare")
Warning: Ignoring 1 observations
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Explore (ggplot)
# # pop_density vs. deathsper100k# cv_states_today_filter %>% # plot_ly(x = ~pop_density, y = ~deathsper100k,# type = 'scatter', mode = 'markers', color = ~state,# size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5))### FINISH CODE HEREp <-ggplot(cv_states_today_filter, aes(x=pop_density, y=deathsper100k, size=population, color = state)) +geom_point() +geom_smooth(method ="lm", se =FALSE) +labs(title ="Population-normalized COVID-19 deaths (per 100k) vs. population density for US states", x ="Population Density", y ="Deaths per 100k") +scale_size_continuous(name ="Population")ggplotly(p)
I think the population density does not appear to be a correlate of new deaths per 100k, as there is not clear trend between population density and deaths, not to mention larger sizes of each point does not correlate with greater deaths.
Multiple line chart
### FINISH CODE HERE# Line chart for naive_CFR for all states over time using `plot_ly()`# add a naive_CFR variable = deaths / cases# cv_states = cv_states %>% mutate(naive_CFR = round((deaths*100/cases),2))plot_ly(cv_states, x =~date, y =~naive_CFR, color =~state, type ="scatter", mode ="lines")
Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
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A bit confusing to interpret this graph, as November 11, 2022 has an extreme value for naive CFR - this means that there was a huge amount of deaths compared to cases (which doesn’t match the other data?). I am unclear on why this is the case.
### FINISH CODE HERE# Line chart for Florida showing new_cases and new_deaths together# add.layer() not found?cv_states %>%filter(state=="Florida") %>%plot_ly(x =~date, y =~new_cases, type ="scatter", mode ="lines") %>%add_trace(x =~date, y =~new_deaths, type ="scatter", mode ="lines")
The peak of cases was January 10, 2022 (84669), with a smaller peak in August 16, 2021 (29711). It is hard to identify the exact peak of deaths, but after zooming in, can be identified as September 20, 2021 (445). There was a delay of around 1 month from August to September, though mildly surprising that the peak of deaths (September 2021) occurred prior to the highest spike in cases (January 2022).
Heatmaps
### FINISH CODE HERE# Map state, date, and new_cases to a matrixlibrary(tidyr)cv_states_mat <- cv_states %>%select(state, date, new_cases) %>% dplyr::filter(date>as.Date("2021-06-15"))cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
When examining newper100k, most states have a noticeable band around January 2022, but Rhode Island stands out the most.
# Create a second heatmap after filtering to only include dates every other weekfilter_dates <-seq(as.Date("2021-06-15"), as.Date("2021-11-01"), by="2 weeks")cv_states_mat <- cv_states %>%select(state, date, newper100k) %>%filter(date %in% filter_dates)cv_states_mat2 <-as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k))rownames(cv_states_mat2) <- cv_states_mat2$datecv_states_mat2$date <-NULLcv_states_mat2 <-as.matrix(cv_states_mat2)# Create a heatmap using plot_ly()plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2),z=~cv_states_mat2,type="heatmap",showscale=T)
Map
### For specified datepick.date ="2021-10-15"# Extract the data for each state by its abbreviationcv_per100 <- cv_states %>%filter(date==pick.date) %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Make sure both maps are on the same color scaleshadeLimit <-125
# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", pick.date), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of ', pick.date, '<br>(Hover for value)'),geo = set_map_details )fig_pick.date <- fig
################ Map for today's date# Extract the data for each state by its abbreviationcv_per100 <- cv_states_today %>%select(state, abb, newper100k, cases, deaths) # select datacv_per100$state_name <- cv_per100$statecv_per100$state <- cv_per100$abbcv_per100$abb <-NULL# Create hover textcv_per100$hover <-with(cv_per100, paste(state_name, '<br>', "Cases per 100k: ", newper100k, '<br>', "Cases: ", cases, '<br>', "Deaths: ", deaths))# Set up mapping detailsset_map_details <-list(scope ='usa',projection =list(type ='albers usa'),showlakes =TRUE,lakecolor =toRGB('white'))# Create the mapfig <-plot_geo(cv_per100, locationmode ='USA-states') %>%add_trace(z =~newper100k, text =~hover, locations =~state,color =~newper100k, colors ='Purples' )fig <- fig %>%colorbar(title =paste0("Cases per 100k: ", Sys.Date()), limits =c(0,shadeLimit))fig <- fig %>%layout(title =paste('Cases per 100k by State as of', Sys.Date(), '<br>(Hover for value)'),geo = set_map_details )fig_Today <- fig### Plot together subplot(fig_pick.date, fig_Today, nrows =2, margin = .05)
Naive_CFR is clearly higher on October 15, 2021 with darker shades of purple across numerous states (thereby greater number of deaths/cases), compared to the most recent date with much lighter shades of purple (thereby fewer deaths/cases).